Low-Rank Hankel Signal Model: Numerical Results

Hankel matrices arise in several applications of signal processing, such as tensor decompositions, biomedical signal processing, etc. In general, these techniques rely on digital signals that can be modeled as a linear combination of exponential polynomials. Hence, the Hankel matrix built from these signals presents full rank, equal to the number of poles, ensured under mild constraints. The present work observes other features of the low-rank Hankel model, using singular value decomposition (SVD) to assess rank deficiency. The effects observed may impact blind source separation (BSS) problems.